FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research

With the advent of large language models (LLMs) and multimodal large language models (MLLMs), the potential of retrieval-augmented generation (RAG) has attracted considerable research attention. Various novel algorithms and models have been introduced to enhance different aspects of RAG systems. However, the absence of a standardized framework for implementation, coupled with the inherently complex RAG process, makes it challenging and time-consuming for researchers to compare and evaluate these approaches in a consistent environment. Existing RAG toolkits, such as LangChain and LlamaIndex, while available, are often heavy and inflexibly, failing to meet the customization needs of researchers. In response to this challenge, we develop \ours{}, an efficient and modular open-source toolkit designed to assist researchers in reproducing and comparing existing RAG methods and developing their own algorithms within a unified framework. Our toolkit has implemented 16 advanced RAG methods and gathered and organized 38 benchmark datasets. It has various features, including a customizable modular framework, multimodal RAG capabilities, a rich collection of pre-implemented RAG works, comprehensive datasets, efficient auxiliary pre-processing scripts, and extensive and standard evaluation metrics. Our toolkit and resources are available atthis https URL.

@article{jin2025_2405.13576,
  title={ FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research },
  author={ Jiajie Jin and Yutao Zhu and Guanting Dong and Yuyao Zhang and Xinyu Yang and Chenghao Zhang and Tong Zhao and Zhao Yang and Zhicheng Dou and Ji-Rong Wen },
  journal={arXiv preprint arXiv:2405.13576},
  year={ 2025 }
}